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Analysis of ‘Austin's data portal activity metrics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/data-portal-activity-metricse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Austin's open data portal provides lots of public data about the City of Austin. It also provides portal administrators with behind-the-scenes information about how the portal is used... but that data is mysterious, hard to handle in a spreadsheet, and not located all in one place.

Until now! Authorized city staff used admin credentials to grab this usage data and share it the public. The City of Austin wants to use this data to inform the development of its open data initiative and manage the open data portal more effectively.

This project contains related datasets for anyone to explore. These include site-level metrics, dataset-level metrics, and department information for context. A detailed detailed description of how the files were prepared (along with code) can be found on github here.

Example questions to answer about the data portal

1. What parts of the open data portal do people seem to value most?
2. What can we tell about who our users are?
3. How are our data publishers doing?
4. How much data is published programmatically vs manually?
5. How data is super fresh? Super stale?
6. Whatever you think we should know...

About the files

all_views_20161003.csv

There is a resource available to portal administrators called "Dataset of datasets". This is the export of that resource, and it was captured on Oct 3, 2016. It contains a summary of the assets available on the data portal. While this file contains over 1400 resources (such as views, charts, and binary files), only 363 are actual tabular datasets.

table_metrics_ytd.csv

This file contains information about the 363 tabular datasets on the portal. Activity metrics for an individual dataset can be accessed by calling Socrata's views/metrics API and passing along the dataset's unique ID, a time frame, and admin credentials. The process of obtaining the 363 identifiers, calling the API, and staging the information can be reviewed in the python notebook here.

site_metrics.csv

This file is the export of site-level stats that Socrata generates using a given time frame and grouping preference. This file contains records about site usage each month from Nov 2011 through Sept 2016. By the way, it contains 285 columns... and we don't know what many of them mean. But we are determined to find out!! For a preliminary exploration of the columns and what portal-related business processes to which they might relate, check out the notes in this python notebook here

city_departments_in_current_budget.csv

This file contains a list of all City of Austin departments according to how they're identified in the most recently approved budget documents. Could be helpful for getting to know more about who the publishers are.

crosswalk_to_budget_dept.csv

The City is in the process of standardizing how departments identify themselves on the data portal. In the meantime, here's a crosswalk from the department values observed in all_views_20161003.csv to the department names that appear in the City's budget

This dataset was created by Hailey Pate and contains around 100 samples along with Di Sync Success, Browser Firefox 19, technical information and other features such as: - Browser Firefox 33 - Di Sync Failed - and more.

How to use this dataset

• Analyze Sf Query Error User in relation to Js Page View Admin
• Study the influence of Browser Firefox 37 on Datasets Created
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Hailey Pate

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The dataset consists of three different data sources:

1. DoH enabled Firefox
2. DoH enabled Google Chrome
3. Cloudflared DoH proxy

The capture of web browser data was made using the Selenium framework, which simulated classical user browsing. The browsers received command for visiting domains taken from Alexa's top 10K most visited websites. The capturing was performed on the host by listening to the network interface of the virtual machine. Overall the dataset contains almost 5,000 web-page visits by Mozilla and 1,000 pages visited by Chrome.

The Cloudflared DoH proxy was installed in Raspberry PI, and the IP address of the Raspberry was set as the default DNS resolver in two separate offices in our university. It was continuously capturing the DNS/DoH traffic created up to 20 devices for around three months.

The dataset contains 1,128,904 flows from which is around 33,000 labeled as DoH. We provide raw pcap data, CSV with flow data, and CSV file with extracted features.

The CSV with extracted features has the following data fields:

- Label (1 - Doh, 0 - regular HTTPS)
- Data source
- Duration
- Minimal Inter-Packet Delay
- Maximal Inter-Packet Delay
- Average Inter-Packet Delay
- A variance of Incoming Packet Sizes
- A variance of Outgoing Packet Sizes
- A ratio of the number of Incoming and outgoing bytes
- A ration of the number of Incoming and outgoing packets
- Average of Incoming Packet sizes
- Average of Outgoing Packet sizes
- The median value of Incoming Packet sizes
- The median value of outgoing Packet sizes
- The ratio of bursts and pauses
- Number of bursts
- Number of pauses
- Autocorrelation
- Transmission symmetry in the 1st third of connection
- Transmission symmetry in the 2nd third of connection
- Transmission symmetry in the last third of connection

The observed network traffic does not contain privacy-sensitive information.

The zip file structure is:

|-- data
|  |-- extracted-features...extracted features used in ML for DoH recognition
|  |  |-- chrome
|  |  |-- cloudflared
|  |  `-- firefox
|  |-- flows...............................................exported flow data
|  |  |-- chrome
|  |  |-- cloudflared
|  |  `-- firefox
|  `-- pcaps....................................................raw PCAP data
|    |-- chrome
|    |-- cloudflared
|    `-- firefox
|-- LICENSE
`-- README.md

When using this dataset, please cite the original work as follows:

@inproceedings{vekshin2020,
author = {Vekshin, Dmitrii and Hynek, Karel and Cejka, Tomas},
title = {DoH Insight: Detecting DNS over HTTPS by Machine Learning},
year = {2020},
isbn = {9781450388337},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3407023.3409192},
doi = {10.1145/3407023.3409192},
booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security},
articleno = {87},
numpages = {8},
keywords = {classification, DoH, DNS over HTTPS, machine learning, detection, datasets},
location = {Virtual Event, Ireland},
series = {ARES '20}
}

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Data Set Information: The experiments have been carried out with a group of 115 students of first-year, undergraduate Engineering major of the University of Genoa. We carried out this study over a simulation environment named Deeds (Digital Electronics Education and Design Suite) which is used for e-learning in digital electronics. The environment provides learning materials through specialized browsers for the students, and asks them to solve various problems with different levels of difficulty. For more information about the Deeds simulator used for this course look at: [Web Link] and to know more about the exercises contents of each session see exercises_info.txt . Our data set contains the students time series of activities during six sessions of laboratory sessions of the course of digital electronics. There are 6 folders containing the students’ data per session. Each Session folder contains up to 99 CSV files each dedicated to a specific student log during that ses

ABSTRACT
End-to-end (E2E) testing is a software validation approach that simulates realistic user scenarios throughout the entire workflow of an application. In the context of web
applications, E2E testing involves two activities: Graphic User Interface (GUI) testing, which simulates user interactions with the web app’s GUI through web browsers, and performance testing, which evaluates system workload handling. Despite its recognized importance in delivering high-quality web applications, the availability of large-scale datasets featuring real-world E2E web tests remains limited, hindering research in the field.
To address this gap, we present E2EGit, a comprehensive dataset of non-trivial open-source web projects collected on GitHub that adopt E2E testing. By analyzing over 5,000 web repositories across popular programming languages (JAVA, JAVASCRIPT, TYPESCRIPT, and PYTHON), we identified 472 repositories implementing 43,670 automated Web GUI tests with popular browser automation frameworks (SELENIUM, PLAYWRIGHT, CYPRESS, PUPPETEER), and 84 repositories that featured 271 automated performance tests implemented leveraging the most popular open-source tools (JMETER, LOCUST). Among these, 13 repositories implemented both types of testing for a total of 786 Web GUI tests and 61 performance tests.

DATASET DESCRIPTION
The dataset is provided as an SQLite database, whose structure is illustrated in Figure 3 (in the paper), which consists of five tables, each serving a specific purpose.
The repository table contains information on 1.5 million repositories collected using the SEART tool on May 4. It includes 34 fields detailing repository characteristics. The
non_trivial_repository table is a subset of the previous one, listing repositories that passed the two filtering stages described in the pipeline. For each repository, it specifies whether it is a web repository using JAVA, JAVASCRIPT, TYPESCRIPT, or PYTHON frameworks. A repository may use multiple frameworks, with corresponding fields (e.g., is web java) set to true, and the field web dependencies listing the detected web frameworks. For Web GUI testing, the dataset includes two additional tables; gui_testing_test _details, where each row represents a test file, providing the file path, the browser automation framework used, the test engine employed, and the number of tests implemented in the file. gui_testing_repo_details, aggregating data from the previous table at the repository level. Each of the 472 repositories has a row summarizing
the number of test files using frameworks like SELENIUM or PLAYWRIGHT, test engines like JUNIT, and the total number of tests identified. For performance testing, the performance_testing_test_details table contains 410 rows, one for each test identified. Each row includes the file path, whether the test uses JMETER or LOCUST, and extracted details such as the number of thread groups, concurrent users, and requests. Notably, some fields may be absent—for instance, if external files (e.g., CSVs defining workloads) were unavailable, or in the case of Locust tests, where parameters like duration and concurrent users are specified via the command line.

To cite this article refer to this citation:

@inproceedings{di2025e2egit,
title={E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects},
author={Di Meglio, Sergio and Starace, Luigi Libero Lucio and Pontillo, Valeria and Opdebeeck, Ruben and De Roover, Coen and Di Martino, Sergio},
booktitle={2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR)},
pages={10--15},
year={2025},
organization={IEEE/ACM}
}

This work has been partially supported by the Italian PNRR MUR project PE0000013-FAIR.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

• The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

• The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

• The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

• ASNs with values >= 500.000

• IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022)
Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono.
ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022,
 author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi},
 title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}},
 journal = {{ACM} {Transactions} on {Privacy} and {Security}},
 doi = {10.1145/3546069},
 publisher = {ACM},
 year  = {2022}
}

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

1. Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

For Login DuckDuckGo Please Visit: 👉 DuckDuckGo Login Account

In today’s digital age, privacy has become one of the most valued aspects of online activity. With increasing concerns over data tracking, surveillance, and targeted advertising, users are turning to privacy-first alternatives for everyday browsing. One of the most recognized names in private search is DuckDuckGo. Unlike mainstream search engines, DuckDuckGo emphasizes anonymity and transparency. However, many people wonder: Is there such a thing as a "https://duckduckgo-account.blogspot.com/ ">DuckDuckGo login account ?

In this comprehensive guide, we’ll explore everything you need to know about the DuckDuckGo login account, what it offers (or doesn’t), and how to get the most out of DuckDuckGo’s privacy features.

Does DuckDuckGo Offer a Login Account? To clarify up front: DuckDuckGo does not require or offer a traditional login account like Google or Yahoo. The concept of a DuckDuckGo login account is somewhat misleading if interpreted through the lens of typical internet services.

DuckDuckGo's entire business model is built around privacy. The company does not track users, store personal information, or create user profiles. As a result, there’s no need—or intention—to implement a system that asks users to log in. This stands in stark contrast to other search engines that rely on login-based ecosystems to collect and use personal data for targeted ads.

That said, some users still search for the term DuckDuckGo login account, usually because they’re trying to save settings, sync devices, or use features that may suggest a form of account system. Let’s break down what’s possible and what alternatives exist within DuckDuckGo’s platform.

Saving Settings Without a DuckDuckGo Login Account Even without a traditional DuckDuckGo login account, users can still save their preferences. DuckDuckGo provides two primary ways to retain search settings:

Local Storage (Cookies) When you customize your settings on the DuckDuckGo account homepage, such as theme, region, or safe search options, those preferences are stored in your browser’s local storage. As long as you don’t clear cookies or use incognito mode, these settings will persist.

Cloud Save Feature To cater to users who want to retain settings across multiple devices without a DuckDuckGo login account, DuckDuckGo offers a feature called "Cloud Save." Instead of creating an account with a username or password, you generate a passphrase or unique key. This key can be used to retrieve your saved settings on another device or browser.

While it’s not a conventional login system, it’s the closest DuckDuckGo comes to offering account-like functionality—without compromising privacy.

Why DuckDuckGo Avoids Login Accounts Understanding why there is no DuckDuckGo login account comes down to the company’s core mission: to offer a private, non-tracking search experience. Introducing login accounts would:

Require collecting some user data (e.g., email, password)

Introduce potential tracking mechanisms

Undermine their commitment to full anonymity

By avoiding a login system, DuckDuckGo keeps user trust intact and continues to deliver on its promise of complete privacy. For users who value anonymity, the absence of a DuckDuckGo login account is actually a feature, not a flaw.

DuckDuckGo and Device Syncing One of the most commonly searched reasons behind the term DuckDuckGo login account is the desire to sync settings or preferences across multiple devices. Although DuckDuckGo doesn’t use accounts, the Cloud Save feature mentioned earlier serves this purpose without compromising security or anonymity.

You simply export your settings using a unique passphrase on one device, then import them using the same phrase on another. This offers similar benefits to a synced account—without the need for usernames, passwords, or emails.

DuckDuckGo Privacy Tools Without a Login DuckDuckGo is more than just a search engine. It also offers a range of privacy tools—all without needing a DuckDuckGo login account:

DuckDuckGo Privacy Browser (Mobile): Available for iOS and Android, this browser includes tracking protection, forced HTTPS, and built-in private search.

DuckDuckGo Privacy Essentials (Desktop Extension): For Chrome, Firefox, and Edge, this extension blocks trackers, grades websites on privacy, and enhances encryption.

Email Protection: DuckDuckGo recently launched a service that allows users to create "@duck.com" email addresses that forward to their real email—removing trackers in the process. Users sign up for this using a token or limited identifier, but it still doesn’t constitute a full DuckDuckGo login account.

Is a DuckDuckGo Login Account Needed? For most users, the absence of a DuckDuckGo login account is not only acceptable—it’s ideal. You can:

Use the search engine privately

Customize and save settings

Sync preferences across devices

Block trackers and protect email

—all without an account.

While some people may find the lack of a traditional login unfamiliar at first, it quickly becomes a refreshing break from constant credential requests, data tracking, and login fatigue.

The Future of DuckDuckGo Accounts As of now, DuckDuckGo maintains its position against traditional account systems. However, it’s clear the company is exploring privacy-preserving ways to offer more user features—like Email Protection and Cloud Save. These features may continue to evolve, but the core commitment remains: no tracking, no personal data storage, and no typical DuckDuckGo login account.

Final Thoughts While the term DuckDuckGo login account is frequently searched, it represents a misunderstanding of how the platform operates . Unlike other tech companies that monetize personal data, DuckDuckGo has stayed true to its promise of privacy .

ABSTRACT
End-to-End (E2E) testing is a comprehensive approach to validating the functionality of a software application by testing its entire workflow from the user’s perspective, ensuring that all integrated components work together as expected. It is crucial for ensuring the quality and reliability of applications, especially in the web domain, which is often bound by Service Level Agreements (SLAs). This testing involves two key activities:
Graphical User Interface (GUI) testing, which simulates user interactions through browsers, and performance testing, which evaluates system workload handling. Despite its importance, E2E testing is often neglected, and the lack of reliable datasets for Web GUI and performance testing has slowed research progress. This paper addresses these limitations by constructing E2EGit, a comprehensive dataset, cataloging non-trivial open-source web projects on GITHUB that adopt GUI or performance testing.
The dataset construction process involved analyzing over 5k non-trivial web repositories based on popular programming languages (JAVA, JAVASCRIPT TYPESCRIPT PYTHON) to identify: 1) GUI tests based on popular browser automation frameworks (SELENIUM PLAYWRIGHT, CYPRESS, PUPPETEER), 2) performance tests written with the most popular open-source tools (JMETER, LOCUST). After analysis, we identified 472 repositories using web GUI testing, with over 43,000 tests, and 84 repositories using performance testing, with 410 tests.

DATASET DESCRIPTION
The dataset is provided as an SQLite database, whose structure is illustrated in Figure 3 (in the paper), which consists of five tables, each serving a specific purpose.
The repository table contains information on 1.5 million repositories collected using the SEART tool on May 4. It includes 34 fields detailing repository characteristics. The
non_trivial_repository table is a subset of the previous one, listing repositories that passed the two filtering stages described in the pipeline. For each repository, it specifies whether it is a web repository using JAVA, JAVASCRIPT, TYPESCRIPT, or PYTHON frameworks. A repository may use multiple frameworks, with corresponding fields (e.g., is web java) set to true, and the field web dependencies listing the detected web frameworks. For Web GUI testing, the dataset includes two additional tables; gui_testing_test _details, where each row represents a test file, providing the file path, the browser automation framework used, the test engine employed, and the number of tests implemented in the file. gui_testing_repo_details, aggregating data from the previous table at the repository level. Each of the 472 repositories has a row summarizing
the number of test files using frameworks like SELENIUM or PLAYWRIGHT, test engines like JUNIT, and the total number of tests identified. For performance testing, the performance_testing_test_details table contains 410 rows, one for each test identified. Each row includes the file path, whether the test uses JMETER or LOCUST, and extracted details such as the number of thread groups, concurrent users, and requests. Notably, some fields may be absent—for instance, if external files (e.g., CSVs defining workloads) were unavailable, or in the case of Locust tests, where parameters like duration and concurrent users are specified via the command line.

To cite this article refer to this citation:

@inproceedings{di2025e2egit,
title={E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects},
author={Di Meglio, Sergio and Starace, Luigi Libero Lucio and Pontillo, Valeria and Opdebeeck, Ruben and De Roover, Coen and Di Martino, Sergio},
booktitle={2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR)},
pages={10--15},
year={2025},
organization={IEEE/ACM}
}

This work has been partially supported by the Italian PNRR MUR project PE0000013-FAIR.

The Klarna Product Page Dataset is a dataset of publicly available pages corresponding to products sold online on various e-commerce websites. The dataset contains offline snapshots of 51,701 product pages collected from 8,175 distinct merchants across 8 different markets (US, GB, SE, NL, FI, NO, DE, AT) between 2018 and 2019. On each page, analysts labelled 5 elements of interest: the price of the product, its image, its name and the add-to-cart and go-to-cart buttons (if found). These labels are present in the HTML code as an attribute called klarna-ai-label taking one of the values: Price, Name, Main picture, Add to cart and Cart.

The snapshots are available in 3 formats: as MHTML files (~24GB), as WebTraversalLibrary (WTL) snapshots (~7.4GB), and as screeshots (~8.9GB). The MHTML format is less lossy, a browser can render these pages though any Javascript on the page is lost. The WTL snapshots are produced by loading the MHTML pages into a chromium-based browser. To keep the WTL dataset compact, the screenshots of the rendered MTHML are provided separately; here we provide the HTML of the rendered DOM tree and additional page and element metadata with rendering information (bounding boxes of elements, font sizes etc.). The folder structure of the screenshot dataset is identical to the one the WTL dataset and can be used to complete the WTL snapshots with image information. For convenience, the datasets are provided with a train/test split in which no merchants in the test set are present in the training set.

For more information about the contents of the datasets (statistics etc.) please refer to the following TMLR paper.

GitHub Repository

The code needed to re-run the experiments in the publication accompanying the dataset can be accessed here.

Citing

If you found this dataset useful in your research, please cite the paper as follows:

@article{hotti2024the,
title={The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models},
author={Alexandra Hotti and Riccardo Sven Risuleo and Stefan Magureanu and Aref Moradi and Jens Lagergren},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=zz6FesdDbB},
note={}
}

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PC Matic is a powerful antivirus and system optimization software designed to protect your devices from malware, ransomware, and performance issues. If you’ve already created an account, logging into your PC Matic account is simple. Follow the steps below to access your dashboard, manage your devices, or renew your subscription.

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What to Do If You Can't Log In Sometimes, you may encounter issues when trying to log into my PC Matic account. Here’s how to troubleshoot:

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Keeping Your PC Matic Account Secure Security is a top priority for any account, especially when it’s tied to antivirus software. When you log into my PC Matic account, follow these best practices:

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How to Stay Logged In If you're logging in from a secure, personal device and want to avoid entering your credentials every time, most browsers allow you to save login information. You can also check the “Remember Me” box on the login screen to stay signed in longer, depending on your account settings.

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Managing Your PC Matic Account After Login Once you log into my PC Matic account, your dashboard becomes your control hub. Here’s what you can do from there:

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Accessing PC Matic from Multiple Devices You don’t have to be on one device to manage your account. Whether you’re on a desktop, laptop, or tablet, you can PC Matic login account from any internet-connected device. Just use your credentials, and your dashboard will sync with all connected machines. (Toll Free) Number

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To manage your antivirus protection, activate your license, view scan results, or download the software, you must first log into your PC Matic account. This tutorial will assist you in safely and swiftly logging into PC Matic, regardless of whether you are a new user or a frequent visitor.

Go to the official login page for PC Matic. Visit the official login page: 👉 "https://login.maticpcaccount.com/" target="_blank">PC Matic Login Portal

A Comprehensive Guide to PC Matic Login To access your account, take the following actions:

Go to the official login page for PC Matic. Visit the official login page: 👉 "https://pcmatic.readthedocs.io/en/latest/" target="_blank">PC Matic Login Portal

Type in your password and registered email. Make use of the email address and password you set up when you bought or installed the PC Matic software.

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Access to the Dashboard You can access the PC Matic dashboard after logging in, where you can:

Examine scan reports

Control your subscription.

Download the PC Matic program.

Turn on your product key.

Typical PC Matic Login Problems & Solutions ❌ Incorrect Credentials Verify that the email address and password you are using are correct. If necessary, use the "forgot password" option.

The website is not loading. Try using a different browser or device, or make sure your internet connection is steady.

The Reasons for Logging Into Your Computer With Matic Login, you can:

Install the software on your Mac or Windows computers.

Turn on your antivirus software.

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Control renewals and billing

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Do you require additional account assistance? Go to the official PC Matic documentation or use the login portal to get in touch with support.

How to Access Your Computer Matic Account

To manage your antivirus settings, examine reports, and make sure your devices remain safe, you must log into your PC Matic account. The process is easy to follow whether you're creating an account for the first time or returning to it. Every step is outlined in this guide, which also offers practical troubleshooting advice for typical login issues.

Detailed Instructions for Accessing Your PC Matic Account

To swiftly and safely access your account, adhere to these guidelines:

Launch the web browser

First, launch the web browser of your choice. Popular browsers like Google Chrome, Mozilla Firefox, Microsoft Edge, and Safari are compatible with PC Matic. To prevent incompatibilities, make sure your browser is up to date.

Go to PC Matic's official website.

In the address bar, type www.pcmatic.com, then hit "Enter." This will direct you to PC Matic's official website. Websites that are suspicious or unofficial should be avoided as they might not be secure.

Press the "Account Login" button.

Locate the "Account Login" option on the PC Matic homepage. Usually, this can be found in the top-right corner of the screen. To access the login page, click on it.

Type Your Information

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Email Address

The password

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Click "Login" after entering your login information. You will be taken to your PC Matic dashboard, where you can access all of your account's features and services, if the information is accurate.

Solving Typical Login Problems

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Lost Password

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utilizing an old email address. Make sure the one you enter is the one associated with your PC Matic account.

Get help from PC Matic's customer service if you're still having trouble logging in.

Problems with Browser Compatibility

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Lockout of Account

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Advice for a Simple Login Process

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Concluding remarks

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In today's fast-paced business environment, managing mailing and shipping operations with efficiency is critical. Pitney Bowes, a well-known global technology company, has long been a leader in shipping, mailing, and e-commerce solutions for businesses of all sizes. Whether you're a small business owner or part of a large organization, managing your services through the "https://pitneybowesaccount.blogspot.com/ ">Pitney Bowes Login My Account portal allows you to streamline operations, track expenses, and stay in control

This article explores how to use the Pitney Bowes Login My Account portal, its benefits, features, troubleshooting tips, and everything you need to know to make the most of your account.

What Is Pitney Bowes Login My Account? Pitney Bowes Login My Account is the online portal that provides registered users access to a centralized platform where they can manage their Pitney Bowes products and services. From checking account balances and ordering supplies to tracking packages and accessing invoices, this portal is the control center for your business’s shipping and mailing needs.

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Troubleshooting Pitney Bowes Login My Account Issues Even with a streamlined system, users may occasionally face login problems. Here are common issues and how to resolve them:

Forgotten Username or Password If you’ve forgotten your login credentials, simply use the “Forgot Username” or “Forgot Password” link on the sign-in page. Follow the prompts to reset your details securely.

Account Lockout After too many failed login attempts, your account may be temporarily locked. Wait a few minutes or reset your password to regain access.

Incorrect Billing Info If your registration fails due to incorrect customer or billing details, double-check with your Pitney Bowes invoice or contact customer support for assistance.

Browser Compatibility Some browser settings or outdated versions can interfere with account access. Clear your cache or try another browser to fix login problems.

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Use a strong and unique password.

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Matanga Darknet — 2025 Access Guide

As internet censorship intensifies, Shadow Marketplaces remain crucial tools for anonymous transactions. Matanga Darknet is one of the most reliable platforms offering secure deals, wide product selection, and user-friendly interface. This article explains how to access Matanga Darknet, its advantages, and security measures for darknet operations.

Current mirrors for Matanga Darknet

Search engines may block darknet resources, so we've compiled official and backup mirrors:

Clearnet mirror (if Tor unavailable):

https://mat2web.top (use with VPN!)

Official .onion address (Tor Browser only):

http://matanzkgpadqndp44ysejfdwehmy4m22mzevmicoth6ebzequny6ayid.onion/

Backup domain (if main domain blocked):

https://matangaweb.com/ (verified URL)

Important! Always verify site's PGP signature and avoid phishing clones.

Additional links|mirrors

Download Tor Browser

https://www.torproject.org/download/

What is Matanga Darknet? Matanga Darknet is a darknet marketplace operating on the Escrow (escrow) model that guarantees transaction security. Key features: - Anonymous payments (BTC+LTC) - No KYC (no identity verification) - Seller rating system and honest reviews

How to access Matanga Darknet? 1. Install Tor Browser (

Advantages of Matanga Darknet over other marketplaces ? Wide selection of high-quality products ? PGP encryption for communications ? Automatic seller payouts

How to avoid scammers? - Verify .onion addresses through forums (Dread, Telegram channels) - Don't follow links from emails/messengers - Use hardware wallets (Ledger, Trezor)

Future of darknet marketplaces With the development of decentralization technologies (Freenet, I2P), Matanga Darknet plans to implement: - Fully p2p trading without a central server - NFT-based transaction guarantees

Useful links https://flagyl4people.top/matanga-tbilisi-1.php

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